Prostate Gland Segmentation on Prostate Magnetic Resonance Images: An AI Study Using a U-net-based Convolutional Neural Network

Purpose: The utilisation of automated systems for the purpose of zonal segmentation has the potential to enhance the efficiency and standardisation of magnetic resonance imaging (MRI) examinations. Furthermore, the delineation of the total gland and zonal boundaries prior to the application of magnetic resonance imaging (MRGB) has been demonstrated to enhance the sensitivity of targeted biopsies. The aim of this study is to automatically segment the prostate gland, transitional zone (TZ) and periferal zone (PZ) on prostate Magnetic Resonance Imaging (MRI) using a U-net based convolutional neural network (CNN). Methods: This retrospective study included a total of 100 patients who underwent screening with a 1.5T MRI device between January and December 2020. The acquired images were evaluated by a senior radiology resident and converted to .nifti format using the MedSeg.ai platform. Prostate and TZ masks were manually traced, while the remaining area (PZ) was automatically segmented by extracting the TZ mask from the prostate mask. A U-net based CNN algorithm with 7 depth layers was developed. Data from 80 patients were used for training the algorithm, with 10 randomly selected for validation. The remaining data from 20 patients were used for testing. Evaluation metrics applied on the test set included accuracy, mean and median Dice Similarity Coefficient (DSC), mean Hausdorff Distance (HSD), Mean Surface Distance (MSD), mean Relative Absolute Volume (RAV). Results: Mean DSC of 0.91 ± 0,03, 0.87 ± 0,06, 0.70 ± 0.16 and median DSC of 0.92, 0.90, 0.75 were obtained for prostate gland, TZ and PZ segmentation respectively. Mean HSD was 8.58, 9.52, 18.78, MSD was 0.92, 0.84, 1.30 and mean RAV was 3.51, 9.87, 70.57 for the segmentation of aforementioned structures. Conclusion: The developed U-net algorithm performed better in segmenting the prostate and TZ than in previous studies. While the success rate of PZ segmentation was lower, this could be attributed to various factors, as indicated by state-of-the-art methods in deep learning. This study highlights AI's vital role in automating prostate segmentation.